On the challenge of reconstructing level-1 phylogenetic networks from triplets and clusters

Gambette, Philippe, Huber, K. T. and Kelk, S. (2017) On the challenge of reconstructing level-1 phylogenetic networks from triplets and clusters. Journal of Mathematical Biology, 74 (7). 1729–1751. ISSN 0303-6812

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    Abstract

    Phylogenetic networks have gained prominence over the years due to their ability to represent complex non-treelike evolutionary events such as recombination or hybridization. Popular combinatorial objects used to construct them are triplet systems and cluster systems, the motivation being that any network $N$ induces a triplet system $\mathcal R(N)$ and a softwired cluster system $\mathcal S(N)$. Since in real-world studies it cannot be guaranteed that all triplets/softwired clusters induced by a network are available, it is of particular interest to understand whether subsets of $\mathcal R(N)$ or $\mathcal S(N)$ allow one to uniquely reconstruct the underlying network $N$. Here we show that even within the highly restricted yet biologically interesting space of level-1 phylogenetic networks it is not always possible to uniquely reconstruct a level-1 network $N$\kelk{,} even when all triplets in $\mathcal R(N)$ or all clusters in $\mathcal S(N)$ are available. On the positive side, we introduce a reasonably large subclass of level-1 networks the members of which are uniquely determined by their induced triplet/softwired cluster systems. Along the way, we also establish various enumerative results, both positive and negative, including results which show that certain special subclasses of level-1 networks $N$ can be uniquely reconstructed from proper subsets of $\mathcal R(N)$ and $\mathcal S(N)$. We anticipate these results to be of use in the design of algorithms for phylogenetic network inference.

    Item Type: Article
    Additional Information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
    Faculty \ School: Faculty of Science > School of Computing Sciences
    Depositing User: Pure Connector
    Date Deposited: 18 Oct 2016 16:00
    Last Modified: 09 Jan 2019 01:06
    URI: https://ueaeprints.uea.ac.uk/id/eprint/60970
    DOI: 10.1007/s00285-016-1068-3

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